Present day health care information systems suffer from a number of deficiencies. A core shortcoming relates to the preferred data representation model. Many prominent health care information systems represent electronic health records using a hierarchical database model, such as is provided by the MUMPS (“Massachusetts General Hospital Utility Multi-Programming System” or “Multi-User Multi-Programming System”) programming language. MUMPS dates from the 1960s.
The MUMPS programming model provides a hierarchical, schema-free, key-value database. Hierarchical data models can be easy to understand and efficient to process, but can at the same time be inflexible in terms of data modeling, because they can only represent one-to-many relationships between data items.
The MUMPS hierarchical data model stands in contrast to the relational data model, first presented in 1970. (Codd, A Relational Model of Data for Large Shared Data Banks, Communications of the ACM, vol. 13:6, June, 1970.) The relational data model represents data as relations each defined as a set of n-tuples, typically organized as a table. Today, systems that use hierarchical data models have been largely displaced by relational database systems, such as those offered by Microsoft, Oracle, Sybase, IBM, Informix, in addition to various open source projects.
The market domination of relational database systems has yielded corresponding technological advances, including improved programming language support, improved management systems, better development environments, more support tools, and the like. Also, the relational database field benefits from a substantially larger community of skilled database programmers, analysts, and administrators.
Despite the advances of relational database systems, MUMPS is still widely used in some industries, including healthcare. The use of MUMPS presents the healthcare industry with a labor shortage, given the small existing community of skilled developers, system administrators and analysts. Moreover, it is difficult for healthcare organizations to implement or extend existing MUMPS-based systems, given the relatively rudimentary set of associated development environments, tools, interfaces, and the like. As a result, in many cases, healthcare organizations using MUMPS-based electronic health records cannot access their own data very easily, accurately, or efficiently.
In one stop-gap approach to addressing the problem of access to MUMPS-based data, some organizations choose to convert MUMPS-based data (e.g., health records) into relational data stored in commercial relational database systems such as those provided by ORACLE or Microsoft. Such conversion is typically performed via an Extract-Transform-Load (“ETL”) process. ETL processes commonly run overnight and can take 24 hours or more before users can access the data, thereby delaying access to time-critical data. Also, many ETL processes map the incoming data to thousands of tables, resulting in a data model that is cumbersome to understand, use, or modify, even with modern tools and database management environments.
In sum, MUMPS-based electronic health records are largely inaccessible for development by modern-trained database developers, system administrators, and analysts. This inaccessibility results in reduced innovation, increased costs, poorer health outcomes, lower quality of service, and the like.
In addition, typical healthcare information systems also tend to store data in a disaggregated manner. Health records, facilities information (e.g., board and lodging information), personnel information, and accounting/billing information are typically stored in distinct databases. To make matters worse, the distinct databases may use different data formats and/or data access protocols. For example, the health records may be stored using a MUMPS-based system (as described above), while facilities information may be stored in a relational database. Each of these may use different application program interfaces, user interfaces, and the like. The disaggregation of data makes it exceedingly difficult or expensive to perform meaningful cross-sectional analyses, such as identifying implicit care processes, identifying relationships between patients and/or workers, and the like.